Overview

Dataset statistics

Number of variables15
Number of observations6745
Missing cells22821
Missing cells (%)22.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory790.6 KiB
Average record size in memory120.0 B

Variable types

Categorical6
Numeric9

Warnings

country has a high cardinality: 137 distinct values High cardinality
iso_code has a high cardinality: 137 distinct values High cardinality
date has a high cardinality: 94 distinct values High cardinality
source_name has a high cardinality: 81 distinct values High cardinality
source_website has a high cardinality: 133 distinct values High cardinality
total_vaccinations is highly correlated with people_vaccinated and 2 other fieldsHigh correlation
people_vaccinated is highly correlated with total_vaccinations and 1 other fieldsHigh correlation
people_fully_vaccinated is highly correlated with total_vaccinationsHigh correlation
daily_vaccinations_raw is highly correlated with daily_vaccinationsHigh correlation
daily_vaccinations is highly correlated with total_vaccinations and 2 other fieldsHigh correlation
total_vaccinations_per_hundred is highly correlated with people_vaccinated_per_hundred and 1 other fieldsHigh correlation
people_vaccinated_per_hundred is highly correlated with total_vaccinations_per_hundredHigh correlation
people_fully_vaccinated_per_hundred is highly correlated with total_vaccinations_per_hundredHigh correlation
total_vaccinations has 2502 (37.1%) missing values Missing
people_vaccinated has 2960 (43.9%) missing values Missing
people_fully_vaccinated has 4169 (61.8%) missing values Missing
daily_vaccinations_raw has 3169 (47.0%) missing values Missing
daily_vaccinations has 195 (2.9%) missing values Missing
total_vaccinations_per_hundred has 2502 (37.1%) missing values Missing
people_vaccinated_per_hundred has 2960 (43.9%) missing values Missing
people_fully_vaccinated_per_hundred has 4169 (61.8%) missing values Missing
daily_vaccinations_per_million has 195 (2.9%) missing values Missing
total_vaccinations has 75 (1.1%) zeros Zeros
daily_vaccinations_raw has 72 (1.1%) zeros Zeros
total_vaccinations_per_hundred has 131 (1.9%) zeros Zeros
people_vaccinated_per_hundred has 104 (1.5%) zeros Zeros

Reproduction

Analysis started2021-03-19 07:01:21.332254
Analysis finished2021-03-19 07:01:39.458398
Duration18.13 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

country
Categorical

HIGH CARDINALITY

Distinct137
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size52.8 KiB
Wales
 
93
England
 
93
Northern Ireland
 
93
Scotland
 
93
Canada
 
93
Other values (132)
6280 

Length

Max length24
Median length7
Mean length8.057968866
Min length4

Characters and Unicode

Total characters54351
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAlbania
2nd rowAlbania
3rd rowAlbania
4th rowAlbania
5th rowAlbania
ValueCountFrequency (%)
Wales93
 
1.4%
England93
 
1.4%
Northern Ireland93
 
1.4%
Scotland93
 
1.4%
Canada93
 
1.4%
United Kingdom93
 
1.4%
China90
 
1.3%
Russia90
 
1.3%
Israel88
 
1.3%
United States87
 
1.3%
Other values (127)5832
86.5%
2021-03-19T12:31:39.702206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united251
 
3.1%
islands174
 
2.2%
ireland166
 
2.1%
northern102
 
1.3%
kingdom93
 
1.2%
scotland93
 
1.2%
england93
 
1.2%
wales93
 
1.2%
canada93
 
1.2%
china90
 
1.1%
Other values (146)6743
84.4%

Most occurring characters

ValueCountFrequency (%)
a8184
15.1%
n4644
 
8.5%
e4090
 
7.5%
i3828
 
7.0%
r3506
 
6.5%
l2724
 
5.0%
o2501
 
4.6%
d2274
 
4.2%
t2224
 
4.1%
s1994
 
3.7%
Other values (41)18382
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter45219
83.2%
Uppercase Letter7870
 
14.5%
Space Separator1246
 
2.3%
Other Punctuation16
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a8184
18.1%
n4644
10.3%
e4090
9.0%
i3828
8.5%
r3506
 
7.8%
l2724
 
6.0%
o2501
 
5.5%
d2274
 
5.0%
t2224
 
4.9%
s1994
 
4.4%
Other values (15)9250
20.5%
ValueCountFrequency (%)
S981
12.5%
I790
 
10.0%
C766
 
9.7%
A613
 
7.8%
B586
 
7.4%
M546
 
6.9%
L374
 
4.8%
G364
 
4.6%
E328
 
4.2%
R309
 
3.9%
Other values (14)2213
28.1%
ValueCountFrequency (%)
1246
100.0%
ValueCountFrequency (%)
'16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin53089
97.7%
Common1262
 
2.3%

Most frequent character per script

ValueCountFrequency (%)
a8184
15.4%
n4644
 
8.7%
e4090
 
7.7%
i3828
 
7.2%
r3506
 
6.6%
l2724
 
5.1%
o2501
 
4.7%
d2274
 
4.3%
t2224
 
4.2%
s1994
 
3.8%
Other values (39)17120
32.2%
ValueCountFrequency (%)
1246
98.7%
'16
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII54351
100.0%

Most frequent character per block

ValueCountFrequency (%)
a8184
15.1%
n4644
 
8.5%
e4090
 
7.5%
i3828
 
7.0%
r3506
 
6.5%
l2724
 
5.0%
o2501
 
4.6%
d2274
 
4.2%
t2224
 
4.1%
s1994
 
3.7%
Other values (41)18382
33.8%

iso_code
Categorical

HIGH CARDINALITY

Distinct137
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size52.8 KiB
GBR
 
93
OWID_NIR
 
93
OWID_SCT
 
93
OWID_WLS
 
93
CAN
 
93
Other values (132)
6280 

Length

Max length8
Median length3
Mean length3.282431431
Min length3

Characters and Unicode

Total characters22140
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowALB
2nd rowALB
3rd rowALB
4th rowALB
5th rowALB
ValueCountFrequency (%)
GBR93
 
1.4%
OWID_NIR93
 
1.4%
OWID_SCT93
 
1.4%
OWID_WLS93
 
1.4%
CAN93
 
1.4%
OWID_ENG93
 
1.4%
CHN90
 
1.3%
RUS90
 
1.3%
ISR88
 
1.3%
USA87
 
1.3%
Other values (127)5832
86.5%
2021-03-19T12:31:40.004398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
owid_nir93
 
1.4%
owid_sct93
 
1.4%
owid_wls93
 
1.4%
can93
 
1.4%
owid_eng93
 
1.4%
gbr93
 
1.4%
rus90
 
1.3%
chn90
 
1.3%
isr88
 
1.3%
usa87
 
1.3%
Other values (127)5832
86.5%

Most occurring characters

ValueCountFrequency (%)
R1988
 
9.0%
A1604
 
7.2%
N1432
 
6.5%
S1343
 
6.1%
L1315
 
5.9%
I1299
 
5.9%
C1118
 
5.0%
E1104
 
5.0%
U1015
 
4.6%
O1006
 
4.5%
Other values (17)8916
40.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter21759
98.3%
Connector Punctuation381
 
1.7%

Most frequent character per category

ValueCountFrequency (%)
R1988
 
9.1%
A1604
 
7.4%
N1432
 
6.6%
S1343
 
6.2%
L1315
 
6.0%
I1299
 
6.0%
C1118
 
5.1%
E1104
 
5.1%
U1015
 
4.7%
O1006
 
4.6%
Other values (16)8535
39.2%
ValueCountFrequency (%)
_381
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21759
98.3%
Common381
 
1.7%

Most frequent character per script

ValueCountFrequency (%)
R1988
 
9.1%
A1604
 
7.4%
N1432
 
6.6%
S1343
 
6.2%
L1315
 
6.0%
I1299
 
6.0%
C1118
 
5.1%
E1104
 
5.1%
U1015
 
4.7%
O1006
 
4.6%
Other values (16)8535
39.2%
ValueCountFrequency (%)
_381
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII22140
100.0%

Most frequent character per block

ValueCountFrequency (%)
R1988
 
9.0%
A1604
 
7.2%
N1432
 
6.5%
S1343
 
6.1%
L1315
 
5.9%
I1299
 
5.9%
C1118
 
5.0%
E1104
 
5.0%
U1015
 
4.6%
O1006
 
4.5%
Other values (17)8916
40.3%

date
Categorical

HIGH CARDINALITY

Distinct94
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size52.8 KiB
2021-03-07
 
116
2021-03-01
 
116
2021-03-08
 
116
2021-03-10
 
115
2021-03-04
 
115
Other values (89)
6167 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters67450
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-10
2nd row2021-01-11
3rd row2021-01-12
4th row2021-01-13
5th row2021-01-14
ValueCountFrequency (%)
2021-03-07116
 
1.7%
2021-03-01116
 
1.7%
2021-03-08116
 
1.7%
2021-03-10115
 
1.7%
2021-03-04115
 
1.7%
2021-03-05115
 
1.7%
2021-03-06115
 
1.7%
2021-03-09115
 
1.7%
2021-02-28114
 
1.7%
2021-03-03114
 
1.7%
Other values (84)5594
82.9%
2021-03-19T12:31:40.390380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-03-07116
 
1.7%
2021-03-01116
 
1.7%
2021-03-08116
 
1.7%
2021-03-10115
 
1.7%
2021-03-04115
 
1.7%
2021-03-05115
 
1.7%
2021-03-06115
 
1.7%
2021-03-09115
 
1.7%
2021-02-28114
 
1.7%
2021-03-03114
 
1.7%
Other values (84)5594
82.9%

Most occurring characters

ValueCountFrequency (%)
219174
28.4%
016307
24.2%
-13490
20.0%
111851
17.6%
32660
 
3.9%
5707
 
1.0%
4704
 
1.0%
6674
 
1.0%
8670
 
1.0%
7651
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number53960
80.0%
Dash Punctuation13490
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
219174
35.5%
016307
30.2%
111851
22.0%
32660
 
4.9%
5707
 
1.3%
4704
 
1.3%
6674
 
1.2%
8670
 
1.2%
7651
 
1.2%
9562
 
1.0%
ValueCountFrequency (%)
-13490
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common67450
100.0%

Most frequent character per script

ValueCountFrequency (%)
219174
28.4%
016307
24.2%
-13490
20.0%
111851
17.6%
32660
 
3.9%
5707
 
1.0%
4704
 
1.0%
6674
 
1.0%
8670
 
1.0%
7651
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII67450
100.0%

Most frequent character per block

ValueCountFrequency (%)
219174
28.4%
016307
24.2%
-13490
20.0%
111851
17.6%
32660
 
3.9%
5707
 
1.0%
4704
 
1.0%
6674
 
1.0%
8670
 
1.0%
7651
 
1.0%

total_vaccinations
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct4064
Distinct (%)95.8%
Missing2502
Missing (%)37.1%
Infinite0
Infinite (%)0.0%
Mean2285175.267
Minimum0
Maximum110737856
Zeros75
Zeros (%)1.1%
Memory size52.8 KiB
2021-03-19T12:31:40.525020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1882.7
Q136650
median250000
Q31175071
95-th percentile9187623.7
Maximum110737856
Range110737856
Interquartile range (IQR)1138421

Descriptive statistics

Standard deviation8041932.547
Coefficient of variation (CV)3.519175383
Kurtosis80.6065853
Mean2285175.267
Median Absolute Deviation (MAD)242087
Skewness8.129045652
Sum9695998658
Variance6.467267908 × 1013
MonotocityNot monotonic
2021-03-19T12:31:40.667146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
075
 
1.1%
1244113
 
0.2%
86498
 
0.1%
107027
 
0.1%
674244
 
0.1%
724263
 
< 0.1%
400003
 
< 0.1%
133293
 
< 0.1%
87723
 
< 0.1%
40003
 
< 0.1%
Other values (4054)4121
61.1%
(Missing)2502
37.1%
ValueCountFrequency (%)
075
1.1%
52
 
< 0.1%
121
 
< 0.1%
131
 
< 0.1%
181
 
< 0.1%
ValueCountFrequency (%)
1107378561
< 0.1%
1090818601
< 0.1%
1070602741
< 0.1%
1057035011
< 0.1%
1011280051
< 0.1%

people_vaccinated
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct3625
Distinct (%)95.8%
Missing2960
Missing (%)43.9%
Infinite0
Infinite (%)0.0%
Mean1858055.057
Minimum0
Maximum72135616
Zeros60
Zeros (%)0.9%
Memory size52.8 KiB
2021-03-19T12:31:40.820244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2193.6
Q134772
median230329
Q3942017
95-th percentile7892472.8
Maximum72135616
Range72135616
Interquartile range (IQR)907245

Descriptive statistics

Standard deviation5975595.993
Coefficient of variation (CV)3.216048939
Kurtosis54.62166687
Mean1858055.057
Median Absolute Deviation (MAD)217862
Skewness6.663408402
Sum7032738389
Variance3.570774747 × 1013
MonotocityNot monotonic
2021-03-19T12:31:40.961373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
060
 
0.9%
2477310
 
0.1%
86498
 
0.1%
107028
 
0.1%
570246
 
0.1%
103254
 
0.1%
2500003
 
< 0.1%
6401153
 
< 0.1%
300003
 
< 0.1%
20003
 
< 0.1%
Other values (3615)3677
54.5%
(Missing)2960
43.9%
ValueCountFrequency (%)
060
0.9%
51
 
< 0.1%
131
 
< 0.1%
181
 
< 0.1%
201
 
< 0.1%
ValueCountFrequency (%)
721356161
< 0.1%
710544451
< 0.1%
697842101
< 0.1%
688840111
< 0.1%
659653051
< 0.1%

people_fully_vaccinated
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct2458
Distinct (%)95.4%
Missing4169
Missing (%)61.8%
Infinite0
Infinite (%)0.0%
Mean749927.3195
Minimum1
Maximum39042345
Zeros0
Zeros (%)0.0%
Memory size52.8 KiB
2021-03-19T12:31:41.118459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2039
Q117388
median92566
Q3448936.25
95-th percentile2454456.25
Maximum39042345
Range39042344
Interquartile range (IQR)431548.25

Descriptive statistics

Standard deviation3021858.416
Coefficient of variation (CV)4.029535046
Kurtosis86.05404952
Mean749927.3195
Median Absolute Deviation (MAD)88135
Skewness8.775792438
Sum1931812775
Variance9.131628285 × 1012
MonotocityNot monotonic
2021-03-19T12:31:41.262581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1901911
 
0.2%
122729
 
0.1%
62287
 
0.1%
557676
 
0.1%
83666
 
0.1%
104004
 
0.1%
24
 
0.1%
19284
 
0.1%
54
 
0.1%
97853
 
< 0.1%
Other values (2448)2518
37.3%
(Missing)4169
61.8%
ValueCountFrequency (%)
12
< 0.1%
24
0.1%
54
0.1%
81
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
390423451
< 0.1%
383354321
< 0.1%
374592691
< 0.1%
369297771
< 0.1%
350001591
< 0.1%

daily_vaccinations_raw
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct3319
Distinct (%)92.8%
Missing3169
Missing (%)47.0%
Infinite0
Infinite (%)0.0%
Mean85386.07802
Minimum0
Maximum4575496
Zeros72
Zeros (%)1.1%
Memory size52.8 KiB
2021-03-19T12:31:41.413684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile144.75
Q12625.75
median13446.5
Q357524.25
95-th percentile352953.5
Maximum4575496
Range4575496
Interquartile range (IQR)54898.5

Descriptive statistics

Standard deviation259638.0348
Coefficient of variation (CV)3.040753725
Kurtosis67.18693
Mean85386.07802
Median Absolute Deviation (MAD)12598.5
Skewness7.162530149
Sum305340615
Variance6.741190911 × 1010
MonotocityNot monotonic
2021-03-19T12:31:41.554308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
072
 
1.1%
19
 
0.1%
5345
 
0.1%
304
 
0.1%
84
 
0.1%
9304
 
0.1%
30004
 
0.1%
6544
 
0.1%
10743
 
< 0.1%
903
 
< 0.1%
Other values (3309)3464
51.4%
(Missing)3169
47.0%
ValueCountFrequency (%)
072
1.1%
19
 
0.1%
21
 
< 0.1%
32
 
< 0.1%
41
 
< 0.1%
ValueCountFrequency (%)
45754961
< 0.1%
30393941
< 0.1%
29241121
< 0.1%
29042291
< 0.1%
24826031
< 0.1%

daily_vaccinations
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct4736
Distinct (%)72.3%
Missing195
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean59235.52702
Minimum1
Maximum2541597
Zeros0
Zeros (%)0.0%
Memory size52.8 KiB
2021-03-19T12:31:41.701421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile112.45
Q11089
median6424
Q328231.25
95-th percentile270363.8
Maximum2541597
Range2541596
Interquartile range (IQR)27142.25

Descriptive statistics

Standard deviation196717.9036
Coefficient of variation (CV)3.320944599
Kurtosis57.2600865
Mean59235.52702
Median Absolute Deviation (MAD)6112.5
Skewness6.898057901
Sum387992702
Variance3.869793358 × 1010
MonotocityNot monotonic
2021-03-19T12:31:41.856514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40352
 
0.8%
771247
 
0.7%
27541
 
0.6%
29440
 
0.6%
135137
 
0.5%
7735
 
0.5%
89532
 
0.5%
22231
 
0.5%
112130
 
0.4%
6330
 
0.4%
Other values (4726)6175
91.5%
(Missing)195
 
2.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
25415971
< 0.1%
24350371
< 0.1%
24274301
< 0.1%
23869321
< 0.1%
23028441
< 0.1%

total_vaccinations_per_hundred
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct1713
Distinct (%)40.4%
Missing2502
Missing (%)37.1%
Infinite0
Infinite (%)0.0%
Mean9.118823945
Minimum0
Maximum143.35
Zeros131
Zeros (%)1.9%
Memory size52.8 KiB
2021-03-19T12:31:42.002631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.72
median3.43
Q39.56
95-th percentile36.529
Maximum143.35
Range143.35
Interquartile range (IQR)8.84

Descriptive statistics

Standard deviation16.49915304
Coefficient of variation (CV)1.809350979
Kurtosis18.69235955
Mean9.118823945
Median Absolute Deviation (MAD)3.15
Skewness3.857395855
Sum38691.17
Variance272.2220509
MonotocityNot monotonic
2021-03-19T12:31:42.161409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0131
 
1.9%
0.0159
 
0.9%
0.0243
 
0.6%
0.0534
 
0.5%
0.0333
 
0.5%
0.0430
 
0.4%
0.0625
 
0.4%
0.2925
 
0.4%
0.0724
 
0.4%
0.2422
 
0.3%
Other values (1703)3817
56.6%
(Missing)2502
37.1%
ValueCountFrequency (%)
0131
1.9%
0.0159
0.9%
0.0243
 
0.6%
0.0333
 
0.5%
0.0430
 
0.4%
ValueCountFrequency (%)
143.351
< 0.1%
140.131
< 0.1%
136.942
< 0.1%
135.021
< 0.1%
133.252
< 0.1%

people_vaccinated_per_hundred
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct1446
Distinct (%)38.2%
Missing2960
Missing (%)43.9%
Infinite0
Infinite (%)0.0%
Mean7.02803963
Minimum0
Maximum88.06
Zeros104
Zeros (%)1.5%
Memory size52.8 KiB
2021-03-19T12:31:42.319569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.72
median2.99
Q37.49
95-th percentile31.262
Maximum88.06
Range88.06
Interquartile range (IQR)6.77

Descriptive statistics

Standard deviation11.31155976
Coefficient of variation (CV)1.609490037
Kurtosis12.55254537
Mean7.02803963
Median Absolute Deviation (MAD)2.63
Skewness3.157005363
Sum26601.13
Variance127.9513843
MonotocityNot monotonic
2021-03-19T12:31:42.566436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0104
 
1.5%
0.0155
 
0.8%
0.0240
 
0.6%
0.0529
 
0.4%
0.0428
 
0.4%
0.0328
 
0.4%
0.326
 
0.4%
0.4925
 
0.4%
0.0724
 
0.4%
0.0621
 
0.3%
Other values (1436)3405
50.5%
(Missing)2960
43.9%
ValueCountFrequency (%)
0104
1.5%
0.0155
0.8%
0.0240
 
0.6%
0.0328
 
0.4%
0.0428
 
0.4%
ValueCountFrequency (%)
88.061
< 0.1%
87.951
< 0.1%
87.892
< 0.1%
85.991
< 0.1%
85.872
< 0.1%

people_fully_vaccinated_per_hundred
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct684
Distinct (%)26.6%
Missing4169
Missing (%)61.8%
Infinite0
Infinite (%)0.0%
Mean3.183276398
Minimum0
Maximum55.29
Zeros47
Zeros (%)0.7%
Memory size52.8 KiB
2021-03-19T12:31:42.711280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.36
median1.29
Q32.8225
95-th percentile11.9225
Maximum55.29
Range55.29
Interquartile range (IQR)2.4625

Descriptive statistics

Standard deviation6.950445511
Coefficient of variation (CV)2.183425076
Kurtosis22.37056555
Mean3.183276398
Median Absolute Deviation (MAD)1.1
Skewness4.547805927
Sum8200.12
Variance48.3086928
MonotocityNot monotonic
2021-03-19T12:31:42.860870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0247
 
0.7%
047
 
0.7%
0.0147
 
0.7%
0.0440
 
0.6%
0.0537
 
0.5%
0.0737
 
0.5%
0.0828
 
0.4%
0.2927
 
0.4%
0.3724
 
0.4%
0.121
 
0.3%
Other values (674)2221
32.9%
(Missing)4169
61.8%
ValueCountFrequency (%)
047
0.7%
0.0147
0.7%
0.0247
0.7%
0.0320
0.3%
0.0440
0.6%
ValueCountFrequency (%)
55.291
< 0.1%
52.181
< 0.1%
50.41
< 0.1%
49.581
< 0.1%
49.052
< 0.1%

daily_vaccinations_per_million
Real number (ℝ≥0)

MISSING

Distinct3238
Distinct (%)49.4%
Missing195
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean2652.161832
Minimum0
Maximum54264
Zeros5
Zeros (%)0.1%
Memory size52.8 KiB
2021-03-19T12:31:43.020686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q1363
median1259.5
Q32961
95-th percentile9599.6
Maximum54264
Range54264
Interquartile range (IQR)2598

Descriptive statistics

Standard deviation4250.226795
Coefficient of variation (CV)1.602551829
Kurtosis36.73066343
Mean2652.161832
Median Absolute Deviation (MAD)1023.5
Skewness4.680763122
Sum17371660
Variance18064427.81
MonotocityNot monotonic
2021-03-19T12:31:43.194506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4358
 
0.9%
453247
 
0.7%
46940
 
0.6%
418440
 
0.6%
749239
 
0.6%
135634
 
0.5%
26232
 
0.5%
17829
 
0.4%
18529
 
0.4%
573429
 
0.4%
Other values (3228)6173
91.5%
(Missing)195
 
2.9%
ValueCountFrequency (%)
05
 
0.1%
116
0.2%
213
0.2%
36
 
0.1%
45
 
0.1%
ValueCountFrequency (%)
542648
0.1%
479471
 
< 0.1%
413441
 
< 0.1%
350271
 
< 0.1%
317001
 
< 0.1%

vaccines
Categorical

Distinct26
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size52.8 KiB
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech
1734 
Pfizer/BioNTech
1276 
Oxford/AstraZeneca, Pfizer/BioNTech
923 
Oxford/AstraZeneca
602 
Moderna, Pfizer/BioNTech
305 
Other values (21)
1905 

Length

Max length82
Median length35
Mean length30.79866568
Min length7

Characters and Unicode

Total characters207737
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPfizer/BioNTech
2nd rowPfizer/BioNTech
3rd rowPfizer/BioNTech
4th rowPfizer/BioNTech
5th rowPfizer/BioNTech
ValueCountFrequency (%)
Moderna, Oxford/AstraZeneca, Pfizer/BioNTech1734
25.7%
Pfizer/BioNTech1276
18.9%
Oxford/AstraZeneca, Pfizer/BioNTech923
13.7%
Oxford/AstraZeneca602
 
8.9%
Moderna, Pfizer/BioNTech305
 
4.5%
Sputnik V236
 
3.5%
Sinovac200
 
3.0%
Pfizer/BioNTech, Sinovac162
 
2.4%
Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V151
 
2.2%
Sinopharm/Beijing126
 
1.9%
Other values (16)1030
15.3%
2021-03-19T12:31:43.535697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pfizer/biontech4934
33.5%
oxford/astrazeneca4003
27.2%
moderna2241
15.2%
sinopharm/beijing847
 
5.8%
v833
 
5.7%
sputnik833
 
5.7%
sinovac526
 
3.6%
sinopharm/wuhan161
 
1.1%
johnson&johnson116
 
0.8%
epivaccorona90
 
0.6%
Other values (3)129
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e21030
 
10.1%
r16313
 
7.9%
i14148
 
6.8%
o13451
 
6.5%
a12183
 
5.9%
n10234
 
4.9%
/9979
 
4.8%
c9587
 
4.6%
f8971
 
4.3%
7968
 
3.8%
Other values (29)83873
40.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter143680
69.2%
Uppercase Letter38893
 
18.7%
Other Punctuation17196
 
8.3%
Space Separator7968
 
3.8%

Most frequent character per category

ValueCountFrequency (%)
e21030
14.6%
r16313
11.4%
i14148
9.8%
o13451
9.4%
a12183
8.5%
n10234
7.1%
c9587
 
6.7%
f8971
 
6.2%
h6369
 
4.4%
d6244
 
4.3%
Other values (11)25150
17.5%
ValueCountFrequency (%)
B5815
15.0%
P4968
12.8%
N4968
12.8%
T4968
12.8%
O4003
10.3%
A4003
10.3%
Z4003
10.3%
S2367
6.1%
M2241
 
5.8%
V923
 
2.4%
Other values (4)634
 
1.6%
ValueCountFrequency (%)
/9979
58.0%
,7101
41.3%
&116
 
0.7%
ValueCountFrequency (%)
7968
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin182573
87.9%
Common25164
 
12.1%

Most frequent character per script

ValueCountFrequency (%)
e21030
 
11.5%
r16313
 
8.9%
i14148
 
7.7%
o13451
 
7.4%
a12183
 
6.7%
n10234
 
5.6%
c9587
 
5.3%
f8971
 
4.9%
h6369
 
3.5%
d6244
 
3.4%
Other values (25)64043
35.1%
ValueCountFrequency (%)
/9979
39.7%
7968
31.7%
,7101
28.2%
&116
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII207737
100.0%

Most frequent character per block

ValueCountFrequency (%)
e21030
 
10.1%
r16313
 
7.9%
i14148
 
6.8%
o13451
 
6.5%
a12183
 
5.9%
n10234
 
4.9%
/9979
 
4.8%
c9587
 
4.6%
f8971
 
4.3%
7968
 
3.8%
Other values (29)83873
40.4%

source_name
Categorical

HIGH CARDINALITY

Distinct81
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size52.8 KiB
Ministry of Health
2139 
Government of the United Kingdom
465 
Official data from provinces via covid19tracker.ca
 
93
Official data from local governments via gogov.ru
 
90
Federal Office of Public Health
 
90
Other values (76)
3868 

Length

Max length62
Median length21
Mean length26.3795404
Min length9

Characters and Unicode

Total characters177930
Distinct characters53
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMinistry of Health
2nd rowMinistry of Health
3rd rowMinistry of Health
4th rowMinistry of Health
5th rowMinistry of Health
ValueCountFrequency (%)
Ministry of Health2139
31.7%
Government of the United Kingdom465
 
6.9%
Official data from provinces via covid19tracker.ca93
 
1.4%
Official data from local governments via gogov.ru90
 
1.3%
Federal Office of Public Health90
 
1.3%
National Health Commission90
 
1.3%
Government of Israel88
 
1.3%
Centers for Disease Control and Prevention87
 
1.3%
National Strategic Group on COVID-1985
 
1.3%
Department of Statistics and Health Information83
 
1.2%
Other values (71)3435
50.9%
2021-03-19T12:31:43.876853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of4911
18.9%
health3519
 
13.5%
ministry2235
 
8.6%
government2015
 
7.7%
the723
 
2.8%
national645
 
2.5%
public567
 
2.2%
united465
 
1.8%
kingdom465
 
1.8%
for462
 
1.8%
Other values (127)10033
38.5%

Most occurring characters

ValueCountFrequency (%)
19295
 
10.8%
e15747
 
8.9%
t14577
 
8.2%
n13579
 
7.6%
o12953
 
7.3%
i12155
 
6.8%
a11703
 
6.6%
r9920
 
5.6%
l6734
 
3.8%
f6415
 
3.6%
Other values (43)54852
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter138331
77.7%
Space Separator19295
 
10.8%
Uppercase Letter18815
 
10.6%
Decimal Number806
 
0.5%
Other Punctuation373
 
0.2%
Dash Punctuation310
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
e15747
11.4%
t14577
10.5%
n13579
9.8%
o12953
9.4%
i12155
8.8%
a11703
8.5%
r9920
 
7.2%
l6734
 
4.9%
f6415
 
4.6%
s5373
 
3.9%
Other values (15)29175
21.1%
ValueCountFrequency (%)
H3729
19.8%
M2572
13.7%
G2484
13.2%
S1340
 
7.1%
I1202
 
6.4%
C1137
 
6.0%
P880
 
4.7%
D796
 
4.2%
N750
 
4.0%
K633
 
3.4%
Other values (12)3292
17.5%
ValueCountFrequency (%)
.266
71.3%
,107
28.7%
ValueCountFrequency (%)
1403
50.0%
9403
50.0%
ValueCountFrequency (%)
19295
100.0%
ValueCountFrequency (%)
-310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin157146
88.3%
Common20784
 
11.7%

Most frequent character per script

ValueCountFrequency (%)
e15747
 
10.0%
t14577
 
9.3%
n13579
 
8.6%
o12953
 
8.2%
i12155
 
7.7%
a11703
 
7.4%
r9920
 
6.3%
l6734
 
4.3%
f6415
 
4.1%
s5373
 
3.4%
Other values (37)47990
30.5%
ValueCountFrequency (%)
19295
92.8%
1403
 
1.9%
9403
 
1.9%
-310
 
1.5%
.266
 
1.3%
,107
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII177930
100.0%

Most frequent character per block

ValueCountFrequency (%)
19295
 
10.8%
e15747
 
8.9%
t14577
 
8.2%
n13579
 
7.6%
o12953
 
7.3%
i12155
 
6.8%
a11703
 
6.6%
r9920
 
5.6%
l6734
 
3.8%
f6415
 
3.6%
Other values (43)54852
30.8%

source_website
Categorical

HIGH CARDINALITY

Distinct133
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size52.8 KiB
https://coronavirus.data.gov.uk/details/healthcare
 
465
https://covid19tracker.ca/vaccinationtracker.html
 
93
https://gogov.ru/articles/covid-v-stats
 
90
https://www.chinadaily.com.cn/a/202103/15/WS604efbd6a31024ad0baaf3de.html
 
90
https://datadashboard.health.gov.il/COVID-19/general
 
88
Other values (128)
5919 

Length

Max length230
Median length59
Mean length68.85366938
Min length16

Characters and Unicode

Total characters464418
Distinct characters73
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowhttps://shendetesia.gov.al/covid19-ministria-e-shendetesise-942-te-vaksinuar-3340-testime-872-te-sheruar-681-raste-te-reja-dhe-16-humbje-jete-ne-24-oret-e-fundit/
2nd rowhttps://shendetesia.gov.al/covid19-ministria-e-shendetesise-942-te-vaksinuar-3340-testime-872-te-sheruar-681-raste-te-reja-dhe-16-humbje-jete-ne-24-oret-e-fundit/
3rd rowhttps://shendetesia.gov.al/covid19-ministria-e-shendetesise-942-te-vaksinuar-3340-testime-872-te-sheruar-681-raste-te-reja-dhe-16-humbje-jete-ne-24-oret-e-fundit/
4th rowhttps://shendetesia.gov.al/covid19-ministria-e-shendetesise-942-te-vaksinuar-3340-testime-872-te-sheruar-681-raste-te-reja-dhe-16-humbje-jete-ne-24-oret-e-fundit/
5th rowhttps://shendetesia.gov.al/covid19-ministria-e-shendetesise-942-te-vaksinuar-3340-testime-872-te-sheruar-681-raste-te-reja-dhe-16-humbje-jete-ne-24-oret-e-fundit/
ValueCountFrequency (%)
https://coronavirus.data.gov.uk/details/healthcare465
 
6.9%
https://covid19tracker.ca/vaccinationtracker.html93
 
1.4%
https://gogov.ru/articles/covid-v-stats90
 
1.3%
https://www.chinadaily.com.cn/a/202103/15/WS604efbd6a31024ad0baaf3de.html90
 
1.3%
https://datadashboard.health.gov.il/COVID-19/general88
 
1.3%
https://covid.cdc.gov/covid-data-tracker/#vaccinations87
 
1.3%
https://twitter.com/QNAEnglish/status/137185317819560755885
 
1.3%
https://healthalert.gov.bh/en/84
 
1.2%
https://www.gob.mx/salud/prensa/111-vacunadas-personas-adultas-mayores-de-775-municipios-o-alcaldias?idiom=es83
 
1.2%
https://www.gob.cl/yomevacuno/83
 
1.2%
Other values (123)5497
81.5%
2021-03-19T12:31:44.207003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://coronavirus.data.gov.uk/details/healthcare465
 
6.9%
https://covid19tracker.ca/vaccinationtracker.html93
 
1.4%
https://www.chinadaily.com.cn/a/202103/15/ws604efbd6a31024ad0baaf3de.html90
 
1.3%
https://gogov.ru/articles/covid-v-stats90
 
1.3%
https://datadashboard.health.gov.il/covid-19/general88
 
1.3%
https://covid.cdc.gov/covid-data-tracker/#vaccinations87
 
1.3%
https://twitter.com/qnaenglish/status/137185317819560755885
 
1.3%
https://healthalert.gov.bh/en84
 
1.2%
https://www.gob.cl/yomevacuno83
 
1.2%
https://www.gob.mx/salud/prensa/111-vacunadas-personas-adultas-mayores-de-775-municipios-o-alcaldias?idiom=es83
 
1.2%
Other values (123)5497
81.5%

Most occurring characters

ValueCountFrequency (%)
t35189
 
7.6%
/32725
 
7.0%
a31154
 
6.7%
s27330
 
5.9%
o24763
 
5.3%
i23816
 
5.1%
e22453
 
4.8%
-18933
 
4.1%
c18580
 
4.0%
n15484
 
3.3%
Other values (63)213991
46.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter327553
70.5%
Other Punctuation58358
 
12.6%
Decimal Number48510
 
10.4%
Dash Punctuation18933
 
4.1%
Uppercase Letter9996
 
2.2%
Connector Punctuation644
 
0.1%
Math Symbol424
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t35189
 
10.7%
a31154
 
9.5%
s27330
 
8.3%
o24763
 
7.6%
i23816
 
7.3%
e22453
 
6.9%
c18580
 
5.7%
n15484
 
4.7%
r15060
 
4.6%
d13665
 
4.2%
Other values (17)100059
30.5%
ValueCountFrequency (%)
D1360
13.6%
C1149
11.5%
V866
 
8.7%
M725
 
7.3%
A723
 
7.2%
I607
 
6.1%
O588
 
5.9%
B534
 
5.3%
S497
 
5.0%
H474
 
4.7%
Other values (15)2473
24.7%
ValueCountFrequency (%)
19853
20.3%
96629
13.7%
35422
11.2%
04529
9.3%
84124
8.5%
24048
8.3%
53599
 
7.4%
63572
 
7.4%
43432
 
7.1%
73302
 
6.8%
ValueCountFrequency (%)
/32725
56.1%
.14963
25.6%
:6745
 
11.6%
%3327
 
5.7%
?348
 
0.6%
#167
 
0.3%
&76
 
0.1%
,7
 
< 0.1%
ValueCountFrequency (%)
-18933
100.0%
ValueCountFrequency (%)
_644
100.0%
ValueCountFrequency (%)
=424
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin337549
72.7%
Common126869
 
27.3%

Most frequent character per script

ValueCountFrequency (%)
t35189
 
10.4%
a31154
 
9.2%
s27330
 
8.1%
o24763
 
7.3%
i23816
 
7.1%
e22453
 
6.7%
c18580
 
5.5%
n15484
 
4.6%
r15060
 
4.5%
d13665
 
4.0%
Other values (42)110055
32.6%
ValueCountFrequency (%)
/32725
25.8%
-18933
14.9%
.14963
11.8%
19853
 
7.8%
:6745
 
5.3%
96629
 
5.2%
35422
 
4.3%
04529
 
3.6%
84124
 
3.3%
24048
 
3.2%
Other values (11)18898
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII464392
> 99.9%
None26
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
t35189
 
7.6%
/32725
 
7.0%
a31154
 
6.7%
s27330
 
5.9%
o24763
 
5.3%
i23816
 
5.1%
e22453
 
4.8%
-18933
 
4.1%
c18580
 
4.0%
n15484
 
3.3%
Other values (62)213965
46.1%
ValueCountFrequency (%)
ñ26
100.0%

Interactions

2021-03-19T12:31:27.468198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:27.661917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:27.868110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:28.018740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:28.169332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:28.430639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:28.562285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:28.703779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:28.849157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:28.997656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:29.154109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:29.291184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:29.434352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:29.586410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:29.736088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:29.881061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:30.024137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:30.182947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:30.350042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:30.501191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:30.660936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:30.846445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:30.999998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:31.162571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:31.327155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:31.457811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:31.591417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:31.743045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:31.869718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:32.005311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:32.132970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:32.267610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:32.405246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:32.650698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:32.796828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:32.944432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:33.073088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:33.210720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:33.337381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:33.468032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:33.598683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:33.744293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:33.894505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:34.049945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:34.185994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:34.326136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:34.462056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:34.602080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:34.745624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:34.872287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:35.004171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:35.142218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:35.261245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:35.384162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:35.515100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:35.642092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:35.767338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:35.898021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:36.035656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:36.190293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:36.318945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:36.466552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:36.732095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:36.868729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:37.008356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:37.143993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:37.285886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:37.446165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:37.593858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:37.735479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:37.877100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-19T12:31:38.005756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-19T12:31:44.342688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-19T12:31:44.599967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-19T12:31:44.863290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-19T12:31:45.239093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-19T12:31:45.488220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-19T12:31:38.243122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-19T12:31:38.611699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-19T12:31:38.954916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-03-19T12:31:39.286857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

countryiso_codedatetotal_vaccinationspeople_vaccinatedpeople_fully_vaccinateddaily_vaccinations_rawdaily_vaccinationstotal_vaccinations_per_hundredpeople_vaccinated_per_hundredpeople_fully_vaccinated_per_hundreddaily_vaccinations_per_millionvaccinessource_namesource_website
0AlbaniaALB2021-01-100.00.0NaNNaNNaN0.000.00NaNNaNPfizer/BioNTechMinistry of Healthhttps://shendetesia.gov.al/covid19-ministria-e-shendetesise-942-te-vaksinuar-3340-testime-872-te-sheruar-681-raste-te-reja-dhe-16-humbje-jete-ne-24-oret-e-fundit/
1AlbaniaALB2021-01-11NaNNaNNaNNaN64.0NaNNaNNaN22.0Pfizer/BioNTechMinistry of Healthhttps://shendetesia.gov.al/covid19-ministria-e-shendetesise-942-te-vaksinuar-3340-testime-872-te-sheruar-681-raste-te-reja-dhe-16-humbje-jete-ne-24-oret-e-fundit/
2AlbaniaALB2021-01-12128.0128.0NaNNaN64.00.000.00NaN22.0Pfizer/BioNTechMinistry of Healthhttps://shendetesia.gov.al/covid19-ministria-e-shendetesise-942-te-vaksinuar-3340-testime-872-te-sheruar-681-raste-te-reja-dhe-16-humbje-jete-ne-24-oret-e-fundit/
3AlbaniaALB2021-01-13188.0188.0NaN60.063.00.010.01NaN22.0Pfizer/BioNTechMinistry of Healthhttps://shendetesia.gov.al/covid19-ministria-e-shendetesise-942-te-vaksinuar-3340-testime-872-te-sheruar-681-raste-te-reja-dhe-16-humbje-jete-ne-24-oret-e-fundit/
4AlbaniaALB2021-01-14266.0266.0NaN78.066.00.010.01NaN23.0Pfizer/BioNTechMinistry of Healthhttps://shendetesia.gov.al/covid19-ministria-e-shendetesise-942-te-vaksinuar-3340-testime-872-te-sheruar-681-raste-te-reja-dhe-16-humbje-jete-ne-24-oret-e-fundit/
5AlbaniaALB2021-01-15308.0308.0NaN42.062.00.010.01NaN22.0Pfizer/BioNTechMinistry of Healthhttps://shendetesia.gov.al/covid19-ministria-e-shendetesise-942-te-vaksinuar-3340-testime-872-te-sheruar-681-raste-te-reja-dhe-16-humbje-jete-ne-24-oret-e-fundit/
6AlbaniaALB2021-01-16369.0369.0NaN61.062.00.010.01NaN22.0Pfizer/BioNTechMinistry of Healthhttps://shendetesia.gov.al/covid19-ministria-e-shendetesise-942-te-vaksinuar-3340-testime-872-te-sheruar-681-raste-te-reja-dhe-16-humbje-jete-ne-24-oret-e-fundit/
7AlbaniaALB2021-01-17405.0405.0NaN36.058.00.010.01NaN20.0Pfizer/BioNTechMinistry of Healthhttps://shendetesia.gov.al/covid19-ministria-e-shendetesise-942-te-vaksinuar-3340-testime-872-te-sheruar-681-raste-te-reja-dhe-16-humbje-jete-ne-24-oret-e-fundit/
8AlbaniaALB2021-01-18447.0447.0NaN42.055.00.020.02NaN19.0Pfizer/BioNTechMinistry of Healthhttps://shendetesia.gov.al/covid19-ministria-e-shendetesise-942-te-vaksinuar-3340-testime-872-te-sheruar-681-raste-te-reja-dhe-16-humbje-jete-ne-24-oret-e-fundit/
9AlbaniaALB2021-01-19483.0483.0NaN36.051.00.020.02NaN18.0Pfizer/BioNTechMinistry of Healthhttps://shendetesia.gov.al/covid19-ministria-e-shendetesise-942-te-vaksinuar-3340-testime-872-te-sheruar-681-raste-te-reja-dhe-16-humbje-jete-ne-24-oret-e-fundit/

Last rows

countryiso_codedatetotal_vaccinationspeople_vaccinatedpeople_fully_vaccinateddaily_vaccinations_rawdaily_vaccinationstotal_vaccinations_per_hundredpeople_vaccinated_per_hundredpeople_fully_vaccinated_per_hundreddaily_vaccinations_per_millionvaccinessource_namesource_website
6735ZimbabweZWE2021-03-0732240.032240.0NaN226.01914.00.220.22NaN129.0Sinopharm/BeijingMinistry of Healthhttps://twitter.com/MoHCCZim/status/1371917996193689606
6736ZimbabweZWE2021-03-0835518.035518.0NaN3278.02009.00.240.24NaN135.0Sinopharm/BeijingMinistry of Healthhttps://twitter.com/MoHCCZim/status/1371917996193689606
6737ZimbabweZWE2021-03-0935761.035761.0NaN243.01526.00.240.24NaN103.0Sinopharm/BeijingMinistry of Healthhttps://twitter.com/MoHCCZim/status/1371917996193689606
6738ZimbabweZWE2021-03-1035901.035901.0NaN140.01133.00.240.24NaN76.0Sinopharm/BeijingMinistry of Healthhttps://twitter.com/MoHCCZim/status/1371917996193689606
6739ZimbabweZWE2021-03-1136019.036019.0NaN118.0766.00.240.24NaN52.0Sinopharm/BeijingMinistry of Healthhttps://twitter.com/MoHCCZim/status/1371917996193689606
6740ZimbabweZWE2021-03-1236283.036283.0NaN264.0708.00.240.24NaN48.0Sinopharm/BeijingMinistry of Healthhttps://twitter.com/MoHCCZim/status/1371917996193689606
6741ZimbabweZWE2021-03-1336359.036359.0NaN76.0621.00.240.24NaN42.0Sinopharm/BeijingMinistry of Healthhttps://twitter.com/MoHCCZim/status/1371917996193689606
6742ZimbabweZWE2021-03-1436359.036359.0NaN0.0588.00.240.24NaN40.0Sinopharm/BeijingMinistry of Healthhttps://twitter.com/MoHCCZim/status/1371917996193689606
6743ZimbabweZWE2021-03-1537660.037660.0NaN1301.0306.00.250.25NaN21.0Sinopharm/BeijingMinistry of Healthhttps://twitter.com/MoHCCZim/status/1371917996193689606
6744ZimbabweZWE2021-03-1639550.039550.0NaN1890.0541.00.270.27NaN36.0Sinopharm/BeijingMinistry of Healthhttps://twitter.com/MoHCCZim/status/1371917996193689606